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Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling

机译：基于模糊本体的交通和城市特征评论的情绪分析，以确保出行安全

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Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating, scores for different factors (e.g., safety, low crime rate, cleanliness, etc.). These rating scores are not efficient enough to deliver precise information, whereas reviews or tweets are significant, because they help travelers and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Prot g web ontology language (OWL) and Java, respectively. The experimental results show satisfactory improvement in tweet and review analysis and opinion mining. (C) 2017 Elsevier Ltd. All rights reserved.
机译：在城市地区，尤其是在大城市，交通拥堵正在迅速增加。迄今为止，存在一些基于传感器网络的系统来解决该问题。但是，这些技术在监视整个运输系统和在需要时提供紧急服务方面还不够合适。这些技术需要实时数据和智能方式，才能根据有用信息快速确定交通活动。另外，这些关于城市交通和旅行的现有系统和网站依赖于评级，针对不同因素（例如安全性，犯罪率低，清洁度等）的得分。这些评分得分的效率不足以提供准确的信息，而评论或推文却很重要，因为它们可以帮助旅行者和交通管理人员了解城市的各个方面。但是，旅行者很难阅读，运输系统也很难处理所有评论和推文，以获得与城市需求有关的表达情感。此类问题的最佳解决方案是分析社交网络平台上可用的信息并进行情感分析。另一方面，基于本体的清晰框架无法从推文和评论中提取模糊的信息。因此，它们产生的结果不足。在这方面，本文提出了基于模糊本体的情感分析和基于语义网规则语言（SWRL）规则的决策，以监控交通活动（事故，车辆，街道状况，交通量等），并使城市旅行者的特征极性图。该系统检索与城市特征和交通活动有关的评论和推文。从这些检索的数据中提取出特征意见，然后使用模糊本体确定交通和城市特征极性。分别使用Prot g网络本体语言（OWL）和Java开发了模糊本体和智能系统原型。实验结果表明，在推文和评论分析以及观点挖掘方面取得了令人满意的改进。 （C）2017 Elsevier Ltd.保留所有权利。

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